Title
Where to Build a New Chargers Stadium
Author

Kathleen Wallis
American River College, Geography 350: Data Acquisition in GIS; Fall 2003
www.ktinsd@yahoo.com
Abstract

Purpose :To find the best possible location for a new stadium for the San Diego Chargers NFL football team
Introduction

The San Diego Chargers football team has been playing at Qualcomm Stadium (formally Jack Murphy Stadium) since the team's conception in San Diego. It is a very old facility and outdated. Alex Spanos is now threatening to move the team, possibly to Los Angeles unless the city builds a new stadium for his time. Moving the team has become more of a likelihood since the building of the new Petco park was built for the San Diego Padres baseball team and this year Mr. Spanos moved the teams practice camp from the field at San Diego State University in La. Jolla to San Dominguez College in Compton California. Moving the ball club would create a void in San Diego for the sport community and for the economics of the city.
Background

In order to use Spatial analyst to find the plot of land that would be best suited for building a new Chargers stadium I had to set what criteria would have to be met in order to qualify a plot of land. The critera for the best location is as follows.
The land must be
a) Within 2 miles of a major freeway Since traffic is a major factor in San Diego a new stadium would have to have easy access to at least one major freeway.
b) Within 2 miles from a mass transit station San Deigans are increasingly turning to mass transit to help ease the traffic problems. The Amtrak line known as The Coaster is a major mode of transportation between Oceanside and downtown, as well as the light rail system know as the Red Trolley. In recent years the Red Trolley line has been extended to the eastern suburbs and the Trolley Station in Santee was opened earlier this year.
c) Within 2 miles of a major hotel In order to serve the tourist community that would be drawn to a new Stadium, an ideal location would be within 2 miles of a major tourist hotel.
d) Built on obtainable land My first thought was to just look at the area that the new Petco Park baseball Stadium, thinking that a Football Stadium could just piggyback on the infrastructure that has been built for Petco Park, but lookingat the land currently available near the downtown K Street area, I concluded that would not be feasible. Much of the old warehouse district downtown has been rebuilt in the last 5 years since the land for Petco Park was allocated. The railroad lines, convention center, new hotels, Gaslamp District and the Naval yards hem in that area
Methods

I. Downloading files
From the SANDAG (San Diego’s Regional Planning Agency) website: www.sandag.org I downloaded SANDAG's free Digital Boundary Files and Layers. These coverages were In gzip format. For each file, I downloaded the zipfile were expored as e00 ESRI Arc Info coverages, so I unzipped each file and used ArcCatalog’s Arctoolbox to import the E00 Downloading these files from the web turned out to be a time consuming process for some of the files were quite large and took a lot of time to download. I have had very little exposure working with zip file and no experience with converting E00 files into Arc Info Coverages so this was quite the learning experience for me.
The files downloaded from SANDAG were:
County
2000 Landuse
Tourist Attractions
Freeways
Railroads
Railroad Stations
Freeways
In addition I downloaded Tiger files county line data from the ESRI website
The Railroads file has transportation lines for the Red Trolley light rail system and for the Amtrak Coaster Line. I changed the lines for Red Trolley stations to be red and Amtrak Coaster stations to be blue to distinguish between these two transit methods. I did the samefor the Railroads Stations layer.

For the tourist points I deleted all records that were not hotel (i.e. Balboa park, Seaport village ect) leaving me with only hotels.

The landuse coverage had 22 different land type categories. I combined similar landuse types (ie Combined single family, mobile homes and multiple family features to make a “housing” category). For the landuse layer I made a layer where I had only the 10 types of landuse. I lumped together transportation corridors, colleges and institutions, Indian reservations, water and mixed use and made them a “unusable” category.

II. Reclassifying layers
a) Distance to transit stations, hotels and transit stations
Using spatial analyst I made straight line distance layer showing buffer zones radiating out from the transit stations. Each zone had a radius of approximenty 2 miles. I then reclassified this layer using the criteria of 10 being the most desirable area, so I reclassified the buffer zones 10 being around the transit station and numbered down from 10 to 1 as one progressed outward from the transit station. This same number system for suitability of land was used for all layers. The same process as outlined above was performed for distance to freeways and distance to large hotels.
II. Reclassifying landuse parcels
The landuse layer was converted to raster. Each cell was a square 1418 feet by 1418 feet, which is about 1 fourth mile square. The raster layer was then reclassified using the following rating::
Rural, 10 ...............Office Space, 3
Undeveloped,10.....Housing, 3
Agriculture, 8......... Water, 1
Recreation, 6..........Shopping Centers, 1
Industry, 4............. Military grounds, 1
Unusable, 1

III. Finding land Cells containing the most suitable pieces of land were identified by using the raster calculator.
The weighting of the datasets was calculated using the following rating system.
Landuse 0.5 (50%)
Distance from Freeways 0.25 (25%)
Distance from Transit station 0.125 (12.5%)
Distance from Large Hotel 0.125 (12.5%)
The higher the percent the greater weight was given to that factor.
Results

The resulting layer produced a layer rating the suitability of different cells of land. The cells with the highest suitability were given a bright red color to make them stand out. Once the desirable parcels of land were identified, the Tiger files layer containing line data for San Diego county was laid over the landuse cells. This provided a network of streets which I could identify using the Arc Map information button. I identified several streets that were within my target area and then visually evaluated the land via the Terra Server aerial photo imagery. I isolated a cell that seemed to have the biggest, flattest parcel of land within my target area.
Map layout
The rest of the process was routine map layout tasks. I laid out a large, closeup view of the target area. A map of the landuse of the entire San Diego county, simple place identifier map of the county and a map of the Terraserver photo of the identified area.


Analysis
This project was a big learning experience for me. I learned that just because you find data on the web doesn't necessarily mean that the data is read to use or even useable at all. I learned how to downlaod E00 file and how to evaluate data to see if it is applicable to fit the project. One of the things that did not work for my project was doing a buffer zone abound wetland. I had wanted to download a layer that showed all the wetlands, and then make a buffer zone around them so that desirable building site would not disturb any wetland, however I found this task impossible with the data I was able to obtain. The wetland shapefile that I downloaded had over 10,000 entities and when I tried to make buffer zones, the resulting map looked like 10,000 rubber bands laid on top of each other....junk. So I had to abandon that criteria for my analysis.
Conclusions
My spatial analysis query resulted in isolating about 8 cells of land that fit my building criteria. These plots of land were southwest of suburb of Santee which is about 15 miles Northeast of downtown. This fit my expectations since the Red Trolley light rail line has just recently been extended out to the town of Santee. It is also very close to Highway 52 and interstate 15. Also there is still a lot of undeveloped land in that area of San Diego County. .
References
Coverages downloaded thanks to www.sandag.org County Tiger file data thanks to www.esri.com Aerial photos thanks to www.terraserver~usa.com